Mobile ad hoc network is a collection of wireless mobile nodes, which are connected over a wireless medium. There is no pre-existing communication infrastructure (no access points, no
base stations) and the nodes can freely move and self-organize into a network topology. Such a network can contain two or more nodes. Hence, balancing the load in an Ad hoc network is
important because the nodes have limited communication resources such as bandwidth, buffer space and battery power. This paper presents a new approach to load balancing based on
residual energy of nodes for distribute the traffic evenly among the network nodes. We are exploiting the multipath routing protocol AOMDV, which defines link-disjoint paths between the
source and the destination in every route discovery. We add the energy metric for load balancing (ELB-AOMDV). The performance is compared between ELB-AOMDV and LBAOMDV.
2. 272 Computer Science & Information Technology (CS & IT)
The main problems are: node mobility and link failure. However, node mobility provides
dynamic change topology and route breaks occur frequently providing degradation of upstream
on wireless network because not only high loss of packets but also delay occurs to search new
route.
The principal metric is load balancing, one idea, inspired by nature, is to simultaneously use all
available resources. Indeed, if two or more disjoint paths between a source and destination, we
can theoretically achieve throughput equal to the cumulative sum of the rates possible on the
roads separately. You need to have a centralized view of customer loads on all nodes. The use of
this visibility may influence the choice of intermediate nodes to route traffic to the correct
destination. This technique improves network performance. The capacity is thus uniformly spread
across the ad hoc network.
Several solutions have been proposed to determine how best to distribute the traffic on various
roads in the case of single path routing and multipath routing path. We propose and we interesting
approach two is multipath routing.
The rest of the paper is organized as follows. Section II presents and discusses some related work
on load balancing in MANETs. Section III explains LB-AOMDV and ELB-AOMDV routing
protocol. Finally, section IV presents simulation results before concluding in section V.
2. BACKGROAND AND RELATED WORKS
The term “Load balancing” distributes traffic to a nodes set, in order to smooth the network load.
That is to say, divide the total load to the various nodes of the network by sending data as nodes
in a position to respond.
Load balancing aims to increase capacity and fault tolerance of networks. It is necessary to ensure
a good load balancing on all ad hoc network paths. Load balancing to single path routing has been
adopted in Ad hoc networks [2], [3], [4].
The load-balancing technique in ad hoc networks can be generally divided into two types. The
first type is “Traffic-size” based [5-6], in which the load is balanced by attempting to distribute
the traffic evenly among the network nodes. The second type is the “Delay” based [7], in which
the load is balanced by attempting to avoid nodes with high delay. In this paper, the proposed
scheme is applicable to most on-demand routing protocols, either single-path routing or multi-
path routing. It belongs to the “Traffic-size” based type, and will distribute the traffic load evenly
among the nodes in ad hoc network.
Distributing processing and communications activity evenly across a computer network so that no
single device is overwhelmed. Load balancing is especially important for networks where it's
difficult to predict the number of requests that will be issued to a server. Busy Web sites typically
employ two or more Web servers in a load balancing scheme. If one server starts to get swamped,
requests are forwarded to another server with more capacity. Load balancing can also refer to the
communications channels themselves. [8]
In [9], load balancing is Load balancing is a technique to forward the traffic from a source to
destination across multiple paths. With equal load balancing, the traffic is balanced across
multiple equal-cost paths.This can be done on a per-packet basis (packet are sent N equal-costs
paths using a round robbin algorithm).
One of the first solutions provides a simple method, but veryeffective to achieve load balancing in
an ad hoc network, is themechanism LBAR (Load-Aware Destination-Controlled routing for
MANET) [10]is an on-demand routing protocol intended for delay-sensitive applications where
users are most concerned with packet transmission delay. Hence, LBAR focuses on how-to find a
path, which would reflect least traffic, load so that data packets can be routed with least delay.
Thealgorithm has four components: Route Discovery, Path Maintenance, Local Connectivity
Management, cost Function Computation.First Route recovery, the route discovery process is
3. Computer Science & Information Technology (CS & IT) 273
initiated whenever asource node needs to communicate with another node for which it does not
have a known route. The process is divided into two stages: forward and backward.
The LBAR protocol disadvantage of not considering in the costfunction, that calculates, the
number of hops between the sourceand destination. One can envisage scenarios where this
protocolcould push the source to choose a route (path) with a largenumber of nodes less loaded
instead of a route with lightlyloaded nodes.
Another solution is proposed protocolWLAR (Weighted Load Aware Routing) [11] protocol is
proposed. This protocol selects the route based on the information from the neighbor nodes which
are on the route to the destination. In WLAR, a new term traffic load is defined as the product of
average queue size of the interface at the node and the number of sharing nodes which are
declared to influence the transmission of their neighbors. (WLAR)protocol adopts basic AODV
procedure and packet format.In WLAR, each node has to measure its average number of packets
queued in its interface, and then check whether it is a sharing node to its neighbor or not.
Finally, we have another solutions protocol proposed is MALB (Multipath Adaptive Load
balancing).The MALB [12] is framework distributes traffic among multiple paths based on path
statistics with the goal of minimizing congestion and end-to-end delay. The aim is to minimize
total cost (a function of the delay on each node’s M/M/1 queue). This is modelled as a
constrained optimization problem which the authors solve using a gradient projection algorithm.
MALB is located at the source node and assumes that disjoint path have already been established
by multipath routing protocol. There are two phases in the load balancing loaded of MALB.
In this paper, we interestingMultipath on-demand protocols try to alleviate these problems by
computing multiple paths in a single route discoveryattempt. Multiple paths could be formed at
both traffic sourcesas well as at intermediate nodes. New route discovery is needed only when all
paths fail. This reduces both routediscovery latency and routing overheads. Multiple paths can
alsobe used to balance load by forwarding data packets on multiple paths at the same time.
The main idea in AOMDV is to compute multiple paths during route discovery. It is designed
primarily for highly dynamic ad hoc networks where link failures and route breaks occur
frequently [13]. The AOMDV protocol has two main components:
a) A route update rule to establish and maintain multiple loop-free paths at each node.
b) A distributed protocol to find link-disjoint paths.
The load balancing phase tries to equalize the congestion measures of the multipath paths. It uses
packet probing techniques to determine path statistics. The imbalance detecting phase detects
high congestion and returns the system to the load balancing phase. There is a rate vector for each
source destination pair which is dynamically updated using probe packets. Each node measures
its traffic arrival rate periodically. Once a node receives a probe packet from the source, it sends a
reply packet indicating its traffic arrival rate.
3.AOMDV WITH LOAD BANACING
In this section, we present our load balancing implement in routing protocols.
The work of [14] shows that the load is maximum at the network center and the paths most used
by the packets pass through the center nodes of the network. As a result, the network load is
maxat the center and decreases gradually as one moves away from it.The load balancing
mechanism proposed in this section is based on this observation. So it would be sufficient to
distribute theload, in a balanced manner on the network, to find a mechanism that pushes the load
outside the center of the network, byselecting the least loaded paths.
In article [15], the proposed protocol will make the following changes to the existing
AOMDVprotocol
a) Paths are selected depending on the hop countand queue length.
4. 274 Computer Science & Information Technology (CS & IT)
b) Load is to be balanced via alternate paths ifqueue length processes a certain threshold
value.
c) RREQ packets are to forwarded or discardeddepending on the queue length.
In article [16], we proposed AODV Therefore, any node that receives a RREQ message,
calculatesthe cost of the path announced and it will compare the cost of the existing path in its
routing table.
In this article [17], we propose routing AOMDV and AODV interesting were analyzed at varying
workload and the energy consumption at nodal level closely monitored.
This is suggesting that at higher data traffic, load will get balanced in more paths resulting in
energy preservation of nodes in the network.
4. PROPOSED ROUTING PROTCOL
In this section, we present our proactive routing protocol based on the proposed optimized
AOMDV structure
4.1. LB-AOMDV ROUTING PROTCOL
The protocol LB-AOMDV (Load Balancing-AOMDV) sought to maximize the lifetime of the
network and improve the performance obtained by the protocol AOMDV
We propose that any node seeking routes to destinations separated by more than two jumps that
are a priori different from the shortest path but less responsible than this. Indeed, for there may be
a better route than the shortest path, we suggest that the comparison of the optimality of the routes
from the shortest path is done through the following metric:
( )
1
_ _
i P
Min number of flux i
n ∈
∑
Where nbr_of_flux (i) denotes the number of flow through a node i participating in the road P.
Dividing by n, number of hops, ensures that the metric takes into account the number of hops of
the route to adequately assess the burden on roads.
Indeed the number of hops in a route separate two distinguished nodes is different from zero. In
addition, the retroactive action of each term of the cost function on the other ensures that the
minimum is always a shortest path route minimized n (thus maximizing the term 1 / n) but it also
minimizes the sum jumps where the road passes, because these nodes are in the center of the
network.
We chose to implement the routing protocol in ns-2.34 which includes a protocol implementation
AOMDV. For changes to the libraries, we have essentially redefined the structure of headers and
messages RREQ and RREP routing table, to incorporate a new field that will contain the burden
of road announced. This charge is expressed as average size of the routing table.
As for the changes to the source code, he discussed ways to redefine the routing messages RREQ
and RREP messages generation. Thus any node receives a RREQ already received will no longer
automatically refuse, it will calculate the path of the road announced and distribute the data
stream allows the route exists in its routing table according to the following routine.
(1)
5. Computer Science & Information Technology (CS & IT) 275
4.3. ELB-AOMDV ROUTING PROTCOL
The ELB-AOMDV (Energy Load Balancing-AOMDV) is a multipath routing protocol; the
different with LB-AOMDV is novel metric energy consumption of the node and energy of the
path.
Thus, the goal is to reduce the rate of control packet used to maintain the network by
incorporating a mechanism called "LB AOMDV" and to route around nodes have a long lifetime
(more energy)
In our approach, we consider only the energy (energy consumption of the node, energy
consumption and average energy of the road network). However, we also consider the rate of
energy consumption at a time instant i. For each node, we follow the following formula to
calculate the rate of energy consumption:
( ) ( ) ( )cons initial resEnergy i Energy i Energy i= −
Où Energycons: energy consumed by each node i report, Energyinital: initial energy 100 joules
and Energyres: is the level of residual energy calculated at the instant i.
The average energy consumption
( )
( )
1
0
cons
cons
n
i
Energy
Energy i
Moy i
n
−
=
=
∑
5. SIMULATION RESULTS
We conducted extensive simulations, using NS2 [17], to determine the effectiveness of our
routing protocol ELB-AOMDV and compared to the well-known routing protocol LB-
AOMDVand.
Begin
# Initialize routing table
bool Exist =false; bool head=false;
for ( p = p->path_link_next)
if(Exist == false) then
if(p->LB==1) then
(path=p; p-
>LB=0);
if(p->path_link_next!=NULL)
then
(p->path_link_next->LB=1);
else head=true;
Exist=true;
End if
End if
End if
if(head) then
(rt_path_list._first->LB=1);
End if
return path;
End for
(2)
(3)
6. 276 Computer Science & Information Technology (CS & IT)
Table 1, gives the simulation parameters,
TABLE I
SIMULATION PARAMETERS
Parameter Value
Number of nodes 20
Simulation time 500s
Radio range 250m
Velocity 2-10 m/s
Simulation area 900 x 900 m2
Physical layer Bandwidth 2Mb/s
MAC IEEE 802.11b
Initial energy 100 Joule
Tx power, Rx power, Idle power 1.4, 1.2, 0.9 (Watt)
Type of traffic CBR
Sendingfrequency 2 packets/s
Packet size 1024 bytes
To evaluate the performances of our protocol, we focused on two performance metrics: ratio %,
life time and gain % live time. This choice implies that we give the right of way for the nodes that
have the greatest connectivity, then the nodes with more energy and at the end nodes with a good
link quality.
Figure. 1Delivery Ratio % vs. Flow of data.
In the Figure 1shows the simulation results of a topology of 20 nodes, 8 traffic sources and
simulation time of 500s, using the protocols LB-AOMDV and ELB-AOMDV. In terms of
delivery ratio is Ratio of packets received to packets sent. We note that in 8flow, LB-AOMDV is
a relative drop in total from ELB-AOMDV
0
10
20
30
40
50
60
70
80
90
100
Deliverypacket
Flow of data
ELB-AOMDV
LB-AOMDV
7. Computer Science & Information Technology (CS & IT) 277
Figure. 2:life time vs.flow of data.
In the figure 2, we note in all cases of flow ELB-AOMDV is the best then LB-AOMDV life time
in the network.
Figure 3Gain (%) LT ELB-AOMDV compared to LB-AOMDVvs.Flow of data.
In terms of mobility, ELB-AOMDV shows more robust when the network density increase,
because the mechanism of load balancing and metric energy consumption, traffic pushes outside
of the most charges nodes, when even there is a path less charged, avoiding network congestion.
In the terms of life time, we define a better ELB-AOMDV when nodes are mobile, because this
existence of multitude path less charged. In the terms Gain live time, we define a in good health
second routing ELB-AOMDV
6. CONCLUSION
In this study, we proposed a new protocol routing load balancing framework for mobile ad hoc
networks based on an optimized distribution traffic construction.
First, we have proposed a new multipath routing protocolcalled LB-AOMDV with a new metric
which is flow of data to select the less congested routes.
Then, we add energy to our proposal LB-AOMDV protocol which includesdelay and throughput
parameters. It takes advantage ofthe RREQ messages to exchange the essentialinformation to
achieve the energy consumption.
The performance characteristic’s ELB-AOMDV and LB-AOMDV to life time in the ad hoc
network were analyzed at traffic distributed.
To demonstrate the effectiveness of our cybernetic structure, we proposed and implemented a
new a routing protocol ELB-AOMDV based on it. Simulation results show that routing protocol
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0
50
100
150
200
250
300
350
400
Deliverypacket
Flow of data
ELB-AOMDV
LB-AOMDV
8. 278 Computer Science & Information Technology (CS & IT)
ELB-AOMDV optimizes the energy consumption within the network and also has the advantage
of supporting mobility and eliminate thecongestion.
In the prospect work, we would like to present more types of metric to our protocol such as
bandwidth constraint.
ACKNOWLEDGMENT
This work is part of the CAPRAH project 08/U311/4966, supported by the Algerian Ministry of
Higher Education and Scientific Research
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